Automated semantic mapping
AssigneeGM Cruise Holdings LLC
InventorsSiddharth Raina, Matthias Wisniowski, Harsha Rajendra Prasad
Life of this patent
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Abstract
The subject disclosure relates to techniques for improving map labeling and in particular, for automating process of semantically labeling maps used for autonomous vehicle (AV) navigation. In some aspects, a process of the disclosed technology includes steps for receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map, processing the 3D LiDAR map to produce a 2D map, rasterizing the 2D map into a plurality of map tiles, and providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles. Systems and machine-readable media are also provided.
Claims20
A computer-implemented method for generating semantically labeled maps, comprising:
- receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map;
- processing the 3D LiDAR map to produce a 2D map;
- rasterizing the 2D map into a plurality of map tiles;
- and
- providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles.
A system for performing image-object detection comprising:
- one or more processors;
- and
- a computer-readable medium comprising instructions stored therein, which when executed by the processors, cause the processors to perform operations comprising:
- receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map;
- processing the 3D LiDAR map to produce a 2D map;
- rasterizing the 2D map into a plurality of map tiles;
- and
- providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles.
A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the processors to perform operations comprising:
- receiving a three-dimensional (3D) Light Detection and Ranging (LiDAR) map;
- processing the 3D LiDAR map to produce a 2D map;
- rasterizing the 2D map into a plurality of map tiles;
- and
- providing one or more of the map tiles to a machine-learning (ML) labeler, wherein the ML labeler is configured to identify and label image features in at least one of the one or more map tiles.
Dependencies are inferred from the claim text.